Towards real-time two-dimensional wave propagation for articulatory speech synthesis
Bibliographic record
Abstract
The precise simulation of voice production is a challenging task, especially when real-time performances are sought. To fulfill real-time constraints, most articulatory vocal synthesizers have to rely on highly simplified acoustic and anatomical models, based on 1D wave propagation and on the usage of vocal tract area functions. In this work, we present a 2D propagation model, designed to simulate the air flow traveling through the midsagittal contour of the vocal tract. Building on the work by Allen et al. [Andrew Allen and Nikunj Raghuvanshi, “Aerophones in flatland: Interactive wave simulation of wind instruments,” ACM Trans. Graph. 34, Article 134 (2015)], we leverage OpenGL and GPU parallelism for a real-time precise 2D airwave simulation. The domain is divided into cells according to a Finite-Difference Time-Domain scheme and coupled with a self-oscillating two-mass vocal fold model. To investigate the system’s ability to simulate the physiology of the vocal tract and its aerodynamics, two studies are presented. First, we compare the performances in vowel production of our 2D approach with other 1D wave propagation systems in literature, using area functions. Subsequently, this case is extended by replacing area functions with 2D vocal tract contours derived from 3D MRI data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".